Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Fully automatic online geometric calibration for non-circular cone-beam CT orbits using fiducials with unknown placement

BACKGROUND

Cone-beam CT (CBCT) with non-circular scanning orbits can improve image quality for 3D intraoperative image guidance. However, geometric calibration of such scans can be challenging. Existing methods typically require a prior image, specialized phantoms, presumed repeatable orbits, or long computation time.

PURPOSE

We propose a novel fully automatic online geometric calibration algorithm that does not require prior knowledge of fiducial configuration. The algorithm is fast, accurate, and can accommodate arbitrary scanning orbits and fiducial configurations.

METHODS

The algorithm uses an automatic initialization process to eliminate human intervention in fiducial localization and an iterative refinement process to ensure robustness and accuracy. We provide a detailed explanation and implementation of the proposed algorithm. Physical experiments on a lab test bench and a clinical robotic C-arm scanner were conducted to evaluate spatial resolution performance and robustness under realistic constraints.

RESULTS

Qualitative and quantitative results from the physical experiments demonstrate high accuracy, efficiency, and robustness of the proposed method. The spatial resolution performance matched that of our existing benchmark method, which used a 3D-2D registration-based geometric calibration algorithm.

CONCLUSIONS

We have demonstrated an automatic online geometric calibration method that delivers high spatial resolution and robustness performance. This methodology enables arbitrary scan trajectories and should facilitate translation of such acquisition methods in a clinical setting.

Patient-derived PixelPrint phantoms for evaluating clinical imaging performance of a deep learning CT reconstruction algorithm

Objective

Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.

Approach

The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different sized extension rings to mimic a small and medium sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error (RMSE), structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.

Main Results

DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25-83% in the small phantom and by 50-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR with a non-anatomical physics phantom.

Significance

DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.

Notice of Removal: Fourier Diffusion Models: A Method to Control MTF and NPS in Score-Based Stochastic Image Generation

Removed.

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that possess accurate densities and exhibit visually realistic image textures. These qualities are crucial for evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized calcium-doped filament to increase the Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility, and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in visual texture and contrast. Micro-CT analysis revealed minimal variations between prints, with an overall deviation of ± 0.8% in filament line spacing and ± 0.022 mm in line width. Measured differences between patient and phantom were less than 12 HU for soft tissue and 15 HU for bone marrow, and 514 HU for cortical bone. The calcium-doped filament accurately represented bony tissue structures across different X-ray energies in spectral CT (RMSE ranging from ± 3 to ± 28 HU, compared to 400 mg/ml hydroxyapatite). In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

Consistency of spectral results in cardiac dual-source photon-counting CT

We evaluate stability of spectral results at different heart rates, acquisition modes, and cardiac phases in first-generation clinical dual-source photon-counting CT (PCCT). A cardiac motion simulator with a coronary stenosis mimicking a 50% eccentric calcium plaque was scanned at five different heart rates (0, 60-100 bpm) with the three available cardiac scan modes (high pitch prospectively ECG-triggered spiral, prospectively ECG-triggered axial, retrospectively ECG-gated spiral). Subsequently, full width half max (FWHM) of the stenosis, Dice score (DSC) for the stenosed region, and eccentricity of the non-stenosed region were calculated for virtual monoenergetic images (VMI) at 50, 70, and 150 keV and iodine density maps at both diastole and systole. FWHM averaged differences of - 0.20, - 0.28, and - 0.15 mm relative to static FWHM at VMI 150 keV across acquisition parameters for high pitch prospectively ECG-triggered spiral, prospectively ECG-triggered axial, and retrospectively ECG-gated spiral scans, respectively. Additionally, there was no effect of heart rate and acquisition mode on FWHM at diastole (p-values < 0.001). DSC demonstrated similarity among parameters with standard deviations of 0.08, 0.09, 0.11, and 0.08 for VMI 50, 70, and 150 keV, and iodine density maps, respectively, with insignificant differences at diastole (p-values < 0.01). Similarly, eccentricity illustrated small differences across heart rate and acquisition mode for each spectral result. Consistency of spectral results at different heart rates and acquisition modes for different cardiac phase demonstrates the added benefit of spectral results from PCCT to dual-source CT to further increase confidence in quantification and advance cardiovascular diagnostics.

Tunable neural networks for CT image formation

Optimization of CT image quality typically involves balancing variance and bias. In traditional filtered back-projection, this trade-off is controlled by the filter cutoff frequency. In model-based iterative reconstruction, the regularization strength parameter often serves the same function. Deep neural networks (DNNs) typically do not provide this tunable control over output image properties. Models are often trained to minimize the expected mean squared error, which penalizes both variance and bias in image outputs but does not offer any control over the trade-off between the two. We propose a method for controlling the output image properties of neural networks with a new loss function called weighted covariance and bias (WCB). Our proposed method uses multiple noise realizations of the input images during training to allow for separate weighting matrices for the variance and bias penalty terms. Moreover, we show that tuning these weights enables targeted penalization of specific image features with spatial frequency domain penalties. To evaluate our method, we present a simulation study using digital anthropomorphic phantoms, physical simulation of CT measurements, and image formation with various algorithms. We show that the WCB loss function offers a greater degree of control over trade-offs between variance and bias, whereas mean-squared error provides only one specific image quality configuration. We also show that WCB can be used to control specific image properties including variance, bias, spatial resolution, and the noise correlation of neural network outputs. Finally, we present a method to optimize the proposed weights for a spiculated lung nodule shape discrimination task. Our results demonstrate this new image quality can control the image properties of DNN outputs and optimize image quality for task-specific performance.

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that have realistic image texture and densities, which are critical in evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized stone-based filament to increase Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in texture and contrast. Measured differences between patient and phantom were less than 15 HU for soft tissue and bone marrow. The stone-based filament accurately represented bony tissue structures across different X-ray energies, as measured by spectral CT. In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

Design and fabrication of 3D-printed patient-specific soft tissue and bone phantoms for CT imaging

The objective of this study is to create patient-specific phantoms for computed tomography (CT) that have realistic image texture and densities, which are critical in evaluating CT performance in clinical settings. The study builds upon a previously presented 3D printing method (PixelPrint) by incorporating soft tissue and bone structures. We converted patient DICOM images directly into 3D printer instructions using PixelPrint and utilized stone-based filament to increase Hounsfield unit (HU) range. Density was modeled by controlling printing speed according to volumetric filament ratio to emulate attenuation profiles. We designed micro-CT phantoms to demonstrate the reproducibility and to determine mapping between filament ratios and HU values on clinical CT systems. Patient phantoms based on clinical cervical spine and knee examinations were manufactured and scanned with a clinical spectral CT scanner. The CT images of the patient-based phantom closely resembled original CT images in texture and contrast. Measured differences between patient and phantom were less than 15 HU for soft tissue and bone marrow. The stone-based filament accurately represented bony tissue structures across different X-ray energies, as measured by spectral CT. In conclusion, this study demonstrated the possibility of extending 3D-printed patient-based phantoms to soft tissue and bone structures while maintaining accurate organ geometry, image texture, and attenuation profiles.

Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study

In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. Data sets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint's production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03-0.29, using a 1-5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intrareader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint's production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint's ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms. Classification: CT lung phantoms, reader study.

Deep Learning CT Image Restoration using System Blur Models

Restoration of images contaminated by blur is an important processing tool across modalities including computed tomography where the blur induced by various system factors can be complex with dependencies on acquisition and reconstruction protocol, and even be patient-dependent. In many cases, such a blur can be modeled and predicted with high accuracy providing an important input to a classical deconvolution approach. While traditional deblurring methods tend to be highly noise magnifying, deep learning approaches have the potential to improve upon classic performance limits. However, most network architectures base their restoration on data inputs alone without knowledge of the system blur. In this work, we explore a deep learning approach that takes both image inputs as well as information that characterizes the system blur to combine modeling and deep learning approaches. We apply the approach to CT image restoration and compare with an image-only deep learning approach. We find that inclusion of the system blur model improves deblurring performance - suggesting the potential power of the combined modeling and deep learning technique.

Revealing pelvic structures in the presence of metal hip prothesis via non-circular CBCT orbits

As the expansion of Cone Beam CT (CBCT) to new interventional procedures continues, the burdensome challenge of metal artifacts remains. Photon starvation and beam hardening from metallic implants and surgical tools in the field of view can result in the anatomy of interest being partially or fully obscured by imaging artifacts. Leveraging the flexibility of modern robotic CBCT imaging systems, implementing non-circular orbits designed for reducing metal artifacts by ensuring data-completeness during acquisition has become a reality. Here, we investigate using non-circular orbits to reduce metal artifacts arising from metallic hip prostheses when imaging pelvic anatomy. As a first proof-of-concept, we implement a sinusoidal and a double-circle-arc orbit on a CBCT test bench, imaging a physical pelvis phantom, with two metal hip prostheses, housing a 3D-printed iodine-filled radial line-pair target. A standard circular orbit implemented with the CBCT test bench acted as comparator. Imaging data collection and processing, geometric calibration and image reconstruction was completed using in-house developed software programs. Imaging with the standard circular orbit, image artifacts were observed in the pelvic bones and only 33 out of the possible 45 line-pairs of the radial line-pair target were partially resolvable in the reconstructed images. Comparatively, imaging with both the sinusoid and double-circle-arc orbits reduced artifacts in the surrounding anatomy and enabled all 45 line-pairs to be visibly resolved in the reconstructed images. These results indicate the potential of non-circular orbits to assist in revealing previously obstructed structures in the pelvic region in the presence of metal hip prosthesis.

PixelPrint: A collection of three-dimensional printed CT phantoms of different respiratory diseases

Imaging is often a first-line method for diagnostics and treatment. Radiological workflows increasingly mine medical images for quantifiable features. Variability in device/vendor, acquisition protocol, data processing, etc., can dramatically affect quantitative measures, including radiomics. We recently developed a method (PixelPrint) for 3D-printing lifelike computed tomography (CT) lung phantoms, paving the way for future diagnostic imaging standardization. PixelPrint generates phantoms with accurate attenuation profiles and textures by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. The present study introduces a library of 3D printed lung phantoms covering a wide range of lung diseases, including usual interstitial pneumonia with advanced fibrosis, chronic hypersensitivity pneumonitis, secondary tuberculosis, cystic fibrosis, Kaposi sarcoma, and pulmonary edema. CT images of the patient-based phantom are qualitatively comparable to original CT images, both in texture, resolution and contrast levels allowing for clear visualization of even subtle imaging abnormalities. The variety of cases chosen for printing include both benign and malignant pathology causing a variety of alveolar and advanced interstitial abnormalities, both clearly visualized on the phantoms. A comparison of regions of interest revealed differences in attenuation below 6 HU. Identical features on the patient and the phantom have a high degree of geometrical correlation, with differences smaller than the intrinsic spatial resolution of the scans. Using PixelPrint, it is possible to generate CT phantoms that accurately represent different pulmonary diseases and their characteristic imaging features.

Approaches for Three Material Decomposition using a Triple-Layer Flat-Panel Detector

X-ray spectral imaging has been increasingly investigated in radiography and interventional imaging. Flat-panel detectors with more than one detection layer have demonstrated advantages in providing separate soft tissue and bone images. Current dual-layer flat-panel detectors (DL-FPD) have limited capability to further differentiate between iodinated contrast agent and bony/calcified structures. In this work, we investigate a triple-layer flat-panel detector (TL-FPD) and the feasibility of three-material (water/calcium/iodine) decomposition. A physical model of TL-FPD, including system geometry, spectrum sensitivities, blur and noise models was developed. Using simulated triple-layer projections, three-material decompositions were performed using three different processing methods: polynomial-based, model-based, and a machine learning-based method (ResUnet). We find that the polynomial-based method leads to very noisy images with poor differentiation between calcium and iodine maps. The model-based method achieved much lower noise levels than the polynomial-based method but exhibited residual errors between the iodine and calcium channels. The ResUnet method offered the best decompositions among the investigated methods in terms of root mean square error from the ground truth and noise in the material maps. These preliminary results demonstrate the feasibility of three-material decomposition using TL-FPD and suggest a path for clinical translation of single-shot contrast/iodine differentiation in radiography and fluoroscopy.

Three-material decomposition using a dual-layer flat panel detector in the presence of soft tissue motion

Digital subtraction angiography (DSA) is a widely used technique for the visualization of contrast-enhanced structures. However, temporal subtraction DSA is challenged by misregistration artifacts due to patient motion and incomplete separation of iodine contrast agent from background soft tissue and bone. In this work, we propose an approach that allows three-material decomposition using a dual-layer flat panel detector in the presence of soft tissue motion. We assume the calcium signal (bone) remains stationary in the pre- and post-contrast images but allow soft tissues to move freely (e.g. cardiac motion). The dual-layer pre- and post-injection images form and ensemble of four measurements that permits material decomposition of four bases (pre- and post-injection soft tissue, calcium, and iodine). We apply two different processing techniques: 1) a modified lookup table and; 2) a model-based material estimation. These are compared with previously proposed DSA methods using temporal subtraction and hybrid (dual-energy) subtraction. Investigations were performed using an XCAT thorax phantom simulating a breath-hold. The pre- and post-contrast measurements were simulated at different time points within a cardiac cycle. Both the lookup table and model-based algorithms eliminate motion artifact as a result of soft tissue motion and allow good separation of iodine, bone, and soft tissue. While the lookup table algorithm contains high noise at the simulated dose level, the model-based algorithm produced iodine images that allow the visualization of major vessels around the heart. In contrast, traditional temporal DSA is susceptible to subtraction artifacts and hybrid DSA shows increased noise.

Practical workflow for arbitrary non-circular orbits for CT with clinical robotic C-arms

Non-circular orbits in cone-beam CT (CBCT) imaging are increasingly being studied for potential benefits in field-of-view, dose reduction, improved image quality, minimal interference in guided procedures, metal artifact reduction, and more. While modern imaging systems such as robotic C-arms are enabling more freedom in potential orbit designs, practical implementation on such clinical systems remains challenging due to obstacles in critical stages of the workflow, including orbit realization, geometric calibration, and reconstruction. In this work, we build upon previous successes in clinical implementation and address key challenges in the geometric calibration stage with a novel calibration method. The resulting workflow eliminates the need for prior patient scans or dedicated calibration phantoms, and can be conducted in clinically relevant processing times.

PixelPrint: Three-dimensional printing of patient-specific soft tissue and bone phantoms for CT

Patient-based CT phantoms, with realistic image texture and densities, are essential tools for assessing and verifying CT performance in clinical practice. This study extends our previously presented 3D printing solution (PixelPrint) to patient-based phantoms with soft tissue and bone structures. To expand the Hounsfield Unit (HUs) range, we utilize a stone-based filament. Applying PixelPrint, we converted patient DICOM images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. Two different phantoms were designed to demonstrate the high reproducibility of our approach with micro-CT acquisitions, and to determine the mapping between filament line widths and HU values on a clinical CT system. Moreover, a third phantom based on a clinical cervical spine scan was manufactured and scanned with a clinical spectral CT scanner. CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels. Measured differences between patient and phantom are around 10 HU for bone marrow voxels and around 150 HU for cortical bone. In addition, stone-based filament can accurately represent boney tissue structures across the different x-ray energies, as measured by spectral CT. This study demonstrates the feasibility of our 3D-printed patient-based phantoms to be extended to soft-tissue and bone structure while maintaining accurate organ geometry, image texture, and attenuation profiles for spectral CT.

Local Linearity Analysis of Deep Learning CT Denoising Algorithms

The rapid development of deep-learning methods in medical imaging has called for an analysis method suitable for non-linear and data-dependent algorithms. In this work, we investigate a local linearity analysis where a complex neural network can be represented as piecewise linear systems. We recognize that a large number of neural networks consists of alternating linear layers and rectified linear unit (ReLU) activations, and are therefore strictly piecewise linear. We investigated the extent of these locally linear regions by gradually adding perturbations to an operating point. For this work, we explored perturbations based on image features of interest, including lesion contrast, background, and additive noise. We then developed strategies to extend these strictly locally linear regions to include neighboring linear regions with similar gradients. Using these approximately linear regions, we applied singular value decomposition (SVD) analysis to each local linear system to investigate and explain the overall nonlinear and data-dependent behaviors of neural networks. The analysis was applied to an example CT denoising algorithm trained on thorax CT scans. We observed that the strictly local linear regions are highly sensitive to small signal perturbations. Over a range of lesion contrast from 0.007 to 0.04 mm^-1, there is a total of 33992 linear regions. The Jacobians are also shift-variant. However, the Jacobians of neighboring linear regions are very similar. By combining linear regions with similar Jacobians, we narrowed down the number of approximately linear regions to four over lesion contrast from 0.001 to 0.08 mm^-1. The SVD analysis to different linear regions revealed denoising behavior that is highly dependent on the background intensity. Analysis further identified greater amount of noise reduction in uniform regions compared to lesion edges. In summary, the local linearity analysis framework we proposed has the potential for us to better characterize and interpret the non-linear and data-dependent behaviors of neural networks.

Universal orbit design for metal artifact elimination

Objective.Metal artifacts are a persistent problem in CT and cone-beam CT. In this work, we propose to reduce or even eliminate metal artifacts by providing better sampling of data using non-circular orbits.Approach.We treat any measurements intersecting metal as missing data, and aim to design a universal orbit that can generally accommodate arbitrary metal shapes and locations. We adapted a local sampling completeness metric based on Tuy's condition to quantify the extent of sampling in the presence of metal. A maxi-min objective over all possible metal locations was used for orbit design. A simple class of sinusoidal orbits was evaluated as a function of frequencies, maximum tilt angles, and orbital extents. Experimental implementation of these orbits were performed on an imaging bench and evaluated on two phantoms, one containing metal balls and the other containing a pedicle screw assembly for spine fixation. Metal artifact reduction (MAR) performance was compared amongst three approaches: non-circular orbits only, algorithmic correction only, and a combined approach.Main results.Theoretical evaluations of the objective favor sinusoidal orbits with large tilt angles and large orbital extents. Furthermore, orbits that leverage redundant azimuthal angles to sample non-redundant data have better performance, e.g. even or non-integer frequency sinusoids for a 360° acquisition. Experimental data support the trends observed in theoretical evaluations. Reconstructions using even or non-integer frequency orbits present less streaking artifacts and background details with finer resolution, even when multiple metal objects are present and even in the absence of MAR algorithms. The combined approach of non-circular orbits and MAR algorithm yields the best performance. The observed trend in image quality is supported by quantitative measures of sampling and severity of streaking artifact.Significance.This work demonstrates that sinusoidal orbits are generally robust against metal artifacts and can provide an avenue for improved image quality in interventional imaging.

Assessment of Boundary Discrimination Performance in a Printed Phantom

Printed phantoms hold great potential as a tool for examining task-based image quality of x-ray imaging systems. Their ability to produce complex shapes rendered in materials with adjustable attenuation coefficients allows a new level of flexibility in the design of tasks for the evaluation of physical imaging systems. We investigate performance in a fine "boundary discrimination" task in which fine features at the margin of a clearly visible "lesion" are used to classify the lesion as malignant or benign. These tasks are appealing because of their relevance to clinical tasks, and because they typically emphasize higher spatial frequencies relative to more common lesion detection tasks. A 3D printed phantom containing cylindrical shells of varying thickness was used to generate lesions profiles that differed in their edge profiles. This was intended to approximate lesions with indistinct margins that are clinically associated with malignancy. Wall thickness in the phantom ranged from 0.4mm to 0.8mm, which allows for task difficulty to be varied by choosing different thicknesses to represent malignant and benign lesions. The phantom was immersed in a tub filled with water and potassium phosphate to approximate the attenuating background, and imaged repeatedly on a benchtop cone-beam CT scanner. After preparing the image data (reconstruction, ROI Selection, sub-pixel registration), we find that the mean frequency of the lesion profile is 0.11 cyc/mm. The mean frequency of the lesion-difference profile, representative of the discrimination task, is approximately 6 times larger. Model observers show appropriate dose performance in these tasks as well.

Performance Assessment Framework for Neural Network Denoising

The proliferation of deep learning image processing calls for a quantitative image quality assessment framework that is suitable for nonlinear, data-dependent algorithms. In this work, we propose a method to systematically evaluate the system and noise responses such that the nonlinear transfer properties can be mapped out. The method involves sampling of lesion perturbations as a function of size, contrast, as well as clinically relevant features such as shape and texture that may be important for diagnosis. We embed the perturbations in backgrounds of varying attenuation levels, noise magnitude and correlation that are associated with different patient anatomies and imaging protocols. The range of system and noise response are further used to evaluate performance for clinical tasks such as signal detection and classification. We performed the assessment for an example CNN-denoising algorithm for low does lung CT screening. The system response of the CNN-denoising algorithm exhibits highly nonlinear behavior where both contrast and higher order lesion features such as spiculated boundaries are not reliably represented for lesions perturbations with small size and low contrast. The noise properties are potentially highly nonstationary, and should be assumed to be the same between the signal-present and signal-absent images. Furthermore, we observer a high degree dependency of both system and noise response on the background attenuation levels. Inputs around zeros are effectively imposed a non-negativity constraint; transfer properties for higher background levels are highly variable. For a detection task, CNN-denoised images improved detectability index by 16-18% compared to low dose CT inputs. For classification task between spiculated and smooth lesions, CNN-denoised images result in a much larger improvement up to 50%. The performance assessment framework propose in this work can systematically map out the nonlinear transfer functions for deep learning algorithms and can potentially enable robust deployment of such algorithms in medical imaging applications.

PixelPrint: Three-dimensional printing of realistic patient-specific lung phantoms for CT imaging

Phantoms are essential tools for assessing and verifying performance in computed tomography (CT). Realistic patient-based lung phantoms that accurately represent textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D-printing solution to create patient-specific lung phantoms with accurate contrast and textures. PixelPrint converts patient images directly into printer instructions, where density is modeled as the ratio of filament to voxel volume to emulate local attenuation values. For evaluation of PixelPrint, phantoms based on four COVID-19 pneumonia patients were manufactured and scanned with the original (clinical) CT scanners and protocols. Density and geometrical accuracies between phantom and patient images were evaluated for various anatomical features in the lung, and a radiomic feature comparison was performed for mild, moderate, and severe COVID-19 pneumonia patient-based phantoms. Qualitatively, CT images of the patient-based phantoms closely resemble the original CT images, both in texture and contrast levels, with clearly visible vascular and parenchymal structures. Regions-of-interest (ROIs) comparing attenuation demonstrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist revealed a high degree of geometrical correlation between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the images. Radiomic feature analysis revealed high correspondence, with correlations of 0.95-0.99 between patient and phantom images. Our study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate geometry, texture, and contrast that will enable protocol optimization, CT research and development advancements, and generation of ground-truth datasets for radiomic evaluations.

Non-circular CBCT orbit design and realization on a clinical robotic C-arm for metal artifact reduction

Metal artifacts have been a difficult challenge for cone-beam CT (CBCT), especially for intraoperative imaging. Metal surgical tools and implants are often present in the field of view and can attenuate X-rays so heavily that they essentially create a missing-data problem. Recently, an increasing number of intra-operative imaging systems such as robotic C-arms are capable of non-circular orbits for data acquisition. Such trajectories can potentially improve sampling and the degree of data completeness to solve the metal-induced missing-data problem, thereby reducing or eliminating the associated image artifacts. In this work, we extend our prior theoretical and experimental work and implement non-circular orbits for metal artifact reduction on a clinical robotic C-arm (Siemens Artis zeego). To maximize the potential for clinical translation, we restrict our implementation to standard built-in motion and data collection functions, also available on other zeego systems, and work within the physical constraints and limitations on positioning and motion. Customized software tools for data extraction, processing, calibration, and reconstruction are used. We demonstrate example non-circular orbits and the resulting image quality using a phantom containing pedicle screws for spine fixation. As compared with a standard circular CBCT orbit, these non-circular orbits exhibit significantly reduced metal artifacts. These results suggest a high potential for image quality improvements for intraoperative CBCT imaging when metal tools or implants are present in the field-of-view.

Data-dependent Nonlinearity Analysis in CT Denoising CNNs

Recent years have seen the increasing application of deep learning methods in medical imaging formation, processing, and analysis. These methods take advantage of the flexibility of nonlinear neural network models to encode information and features in ways that can outperform conventional approaches. However, because of the nonlinear nature of this processing, images formed by neural networks have properties that are highly data-dependent and difficult to analyze. In particular, the generalizability and robustness of these approaches can be difficult to ascertain. In this work, we analyze a class of neural networks that use only piece-wise linear activation functions. This class of networks can be represented by locally linear systems where the linear properties are highly data-dependent - allowing, for example, estimation of noise in image output via standard propagation methods. We propose a nonlinearity index metric that quantifies the fidelity of a local linear approximation of trained models based on specific input data. We applied this analysis to three example CT denoising CNNs to analytically predict the noise properties in the output images. We found that the proposed nonlinearity metric highly correlates with the accuracy of noise predictions. The analysis proposed in this work provides theoretical understanding of the nonlinear behavior of neural networks and enables performance prediction and quantitation under certain conditions.

Fast CBCT Reconstruction using Convolutional Neural Networks for Arbitrary Robotic C-arm Orbits

Cone-beam CT (CBCT) with non-circular acquisition orbits has the potential to improve image quality, increase the field-of view, and facilitate minimal interference within an interventional imaging setting. Because time is of the essence in interventional imaging scenarios, rapid reconstruction methods are advantageous. Model-Based Iterative Reconstruction (MBIR) techniques implicitly handle arbitrary geometries; however, the computational burden for these approaches is particularly high. The aim of this work is to extend a previously proposed framework for fast reconstruction of non-circular CBCT trajectories. The pipeline combines a deconvolution operation on the backprojected measurements using an approximate, shift-invariant system response prior to processing with a Convolutional Neural Network (CNN). We trained and evaluated the CNN for this approach using 1800 randomized arbitrary orbits. Noisy projection data were formed from 1000 procedurally generated tetrahedral phantoms as well as anthropomorphic data in the form of 800 CT and CBCT images from the Lung Image Database Consortium Image Collection (LIDC). Using this proposed reconstruction pipeline, computation time was reduced by 90% as compared to MBIR with only minor differences in performance. Quantitative comparisons of nRMSE, FSIM and SSIM are reported. Performance was consistent for projection data simulated with acquisition orbits the network has not previously been trained on. These results suggest the potential for fast processing of arbitrary CBCT trajectory data with reconstruction times that are clinically relevant and applicable - facilitating the application of non-circular orbits in CT image-guided interventions and intraoperative imaging.

Three-dimensional printing of patient-specific lung phantoms for CT imaging: Emulating lung tissue with accurate attenuation profiles and textures

PURPOSE

Phantoms are a basic tool for assessing and verifying performance in CT research and clinical practice. Patient-based realistic lung phantoms accurately representing textures and densities are essential in developing and evaluating novel CT hardware and software. This study introduces PixelPrint, a 3D printing solution to create patient-based lung phantoms with accurate attenuation profiles and textures.

METHODS

PixelPrint, a software tool, was developed to convert patient digital imaging and communications in medicine (DICOM) images directly into FDM printer instructions (G-code). Density was modeled as the ratio of filament to voxel volume to emulate attenuation profiles for each voxel, with the filament ratio controlled through continuous modification of the printing speed. A calibration phantom was designed to determine the mapping between filament line width and Hounsfield units (HU) within the range of human lungs. For evaluation of PixelPrint, a phantom based on a single human lung slice was manufactured and scanned with the same CT scanner and protocol used for the patient scan. Density and geometrical accuracy between phantom and patient CT data were evaluated for various anatomical features in the lung.

RESULTS

For the calibration phantom, measured mean HU show a very high level of linear correlation with respect to the utilized filament line widths, (r > 0.999). Qualitatively, the CT image of the patient-based phantom closely resembles the original CT image both in texture and contrast levels (from -800 to 0 HU), with clearly visible vascular and parenchymal structures. Regions of interest comparing attenuation illustrated differences below 15 HU. Manual size measurements performed by an experienced thoracic radiologist reveal a high degree of geometrical correlation of details between identical patient and phantom features, with differences smaller than the intrinsic spatial resolution of the scans.

CONCLUSION

The present study demonstrates the feasibility of 3D-printed patient-based lung phantoms with accurate organ geometry, image texture, and attenuation profiles. PixelPrint will enable applications in the research and development of CT technology, including further development in radiomics.

Generative Adversarial Networks and Radiomics Supervision for Lung Lesion Synthesis

Realistic lesion generation is a useful tool for system evaluation and optimization. In this work, we investigate a data-driven approach for categorical lung lesion generation using public lung CT databases. We propose a generative adversarial network with a Wasserstein discrimination and gradient penalty to stabilize training. We further included conditional inputs such that the network can generate user-specified lesion categories. Novel to our network, we directly incorporated radiomic features in an intermediate supervision step to encourage similar textures between generated and real lesions. We evaluated the network using lung lesions from the Lung Image Database Consortium (LIDC) database. The lesions are divided into two categories: solid vs. non-solid. We performed quantitative evaluation of network performance base on four criteria: 1) overfitting in terms of structural and morphological similarity to the training data, 2) diversity of generated lesions in terms of similarity to other generated data, 3) similarity to real lesions in terms of distribution of example radiomics features, and 4) conditional consistency in terms of classification accuracy using a classifier trained on the training lesions. We imposed a quantitative threshold for similarity based on visual inspection. The percentage of non-solid and solid lesions that satisfy low overfitting and high diversity is 96.9% and 88.6% of non-solid and solid lesions respectively. The distribution of example radiomics features are similar in the generated and real lesions indicated by a low Kullback-Leibler divergence score. Classification accuracy for the generated lesions are comparable with that for the real lesions. The proposed network is a promising approach for data-driven generation of realistic lung lesions.

High-resolution model-based material decomposition in dual-layer flat-panel CBCT

PURPOSE

Spectral CT uses energy-dependent measurements that enable material discrimination in addition to reconstruction of structural information. Flat-panel detectors (FPDs) have been widely used in dedicated and interventional systems to deliver high spatial resolution, volumetric cone-beam CT (CBCT) in compact and OR-friendly designs. In this work, we derive a model-based method that facilitates high-resolution material decomposition in a spectral CBCT system equipped with a prototype dual-layer FPD. Through high-fidelity modeling of multilayer detector, we seek to avoid resolution loss that is present in more traditional processing and decomposition approaches.

METHOD

A physical model for spectral measurements in dual-layer flat-panel CBCT is developed including layer-dependent differences in system geometry, spectral sensitivities, and detector blur (e.g., due to varied scintillator thicknesses). This forward model is integrated into a model-based material decomposition (MBMD) method based on minimization of a penalized weighted least-squared (PWLS) objective function. The noise and resolution performance of this approach was compared with traditional projection-domain decomposition (PDD) and image-domain decomposition (IDD) approaches as well as one-step MBMD with lower-fidelity models that use approximated geometry, projection interpolation, or an idealized system geometry without system blur model. Physical studies using high-resolution three-dimensional (3D)-printed water-iodine phantoms were conducted to demonstrate the high-resolution imaging performance of the compared decomposition methods in iodine basis images and synthetic monoenergetic images.

RESULTS

Physical experiments demonstrate that the MBMD methods incorporating an accurate geometry model can yield higher spatial resolution iodine basis images and synthetic monoenergetic images than PDD and IDD results at the same noise level. MBMD with blur modeling can further improve the spatial-resolution compared with the decomposition results obtained with IDD, PDD, and MBMD methods with lower-fidelity models. Using the MBMD without or with blur model can increase the absolute modulation at 1.75 lp/mm by 10% and 22% compared with IDD at the same noise level.

CONCLUSION

The proposed model-based material decomposition method for a dual-layer flat-panel CBCT system has demonstrated an ability to extend high-resolution performance through sophisticated detector modeling including the layer-dependent blur. The proposed work has the potential to not only facilitate high-resolution spectral CT in interventional and dedicated CBCT systems, but may also provide the opportunity to evaluate different flat-panel design trade-offs including multilayer FPDs with mismatched geometries, scintillator thicknesses, and spectral sensitivities.

Direct reconstruction of anatomical change in low-dose lung nodule surveillance

Purpose: In sequential imaging studies, there exists rich information from past studies that can be used in prior-image-based reconstruction (PIBR) as a form of improved regularization to yield higher-quality images in subsequent studies. PIBR methods, such as reconstruction of difference (RoD), have demonstrated great improvements in the image quality of subsequent anatomy reconstruction even when CT data are acquired at very low-exposure settings. Approach: However, to effectively use information from past studies, two major elements are required: (1) registration, usually deformable, must be applied between the current and prior scans. Such registration is greatly complicated by potential ambiguity between patient motion and anatomical change-which is often the target of the followup study. (2) One must select regularization parameters for reliable and robust reconstruction of features. Results: We address these two major issues and apply a modified RoD framework to the clinical problem of lung nodule surveillance. Specifically, we develop a modified deformable registration approach that enforces a locally smooth/rigid registration around the change region and extend previous analytic expressions relating reconstructed contrast to the regularization parameter and other system dependencies for reliable representation of image features. We demonstrate the efficacy of this approach using a combination of realistic digital phantoms and clinical projection data. Performance is characterized as a function of the size of the locally smooth registration region of interest as well as x-ray exposure. Conclusions: This modified framework is effectively able to separate patient motion and anatomical change to directly highlight anatomical change in lung nodule surveillance.

A CT Denoising Neural Network with Image Properties Parameterization and Control

A wide range of dose reduction strategies for x-ray computed tomography (CT) have been investigated. Recently, denoising strategies based on machine learning have been widely applied, often with impressive results, and breaking free from traditional noise-resolution trade-offs. However, since typical machine learning strategies provide a single denoised image volume, there is no user-tunable control of a particular trade-off between noise reduction and image properties (biases) of the denoised image. This is in contrast to traditional filtering and model-based processing that permits tuning of parameters for a level of noise control appropriate for the specific diagnostic task. In this work, we propose a novel neural network that includes a spatial-resolution parameter as additional input permits explicit control of the noise-bias trade-off. Preliminary results show the ability to control image properties through such parameterization as well as the possibility to tune such parameters for increased detectability in task-based evaluation.

End-to-end Modeling for Predicting and Estimating Radiomics: Application to Gray Level Co-occurrence Matrices in CT

While radiomics models are finding increased use in computer-aided diagnostics and as imaging biomarkers for inference and discovery, their utility in computed tomography (CT) is limited by variability of the image properties produced by different CT scanners, imaging protocols, patient anatomy, and an increasingly diverse range of reconstruction and post-processing software. While these effects can be mitigated with careful data curation and standardization of protocols, this is impractical for diverse sources of image data. In this work, we propose to generalize traditional end-to-end imaging system models to include radiomics calculation as an explicit stage. Such a model permits both prediction of the undesirable variability of radiomics, but also forms a basis for inverting the process to estimate the true underlying radiomics. This framework has the potential to provide for standardization of radiomics across imaging conditions permitting more widespread application of radiomics models; larger, more diverse image databases; and improved diagnoses and inferences based on those standardized metrics. We apply this framework to a large class of popular radiomics based on the Gray Level Co-occurrence matrix under conditions of imaging system that are well describe by traditional linear systems approaches as well as nonlinear systems for which traditional analytic models do not apply.

Grating-based Spectral CT using Small Angle X-ray Beam Deflections

Interest in spectral CT for diagnostics and therapy evaluation has been growing. Acquisitions of data from distinct energy spectra provide, among other advantages, quantitative density estimations for multiple materials. We introduce a novel spectral CT concept that includes a fine-pitch grating for prefiltration of the x-ray beam. The attenuation behavior of this grating changes significantly if x-rays are slightly angled in relation to the grating structures. To apply such an angle (i.e. switch between the different filtrations) we propose a fast, controllable, and precise solution by moving the focal spot of the x-ray tube. In this work, we performed preliminary evaluations with a grating prototype on a CT test bench. Our results include x-ray spectrometer measurements that reveal diverse and controllable spectral shaping between 4° and 6° for a specific grating design. Additional experiments with a contrast agent phantom illustrated the capability to decompose clinically relevant iodine concentrations (5, 10, 20, and 50mg/mL) - demonstrating the feasibility of the grating-based approach. Ongoing and future studies will investigate the potential of this novel concept as a relatively simple upgrade to standard energy-integrating CT.

Metal-Tolerant Noncircular Orbit Design and Implementation on Robotic C-Arm Systems

Metal artifacts are a major confounding factor for image quality in CT, especially in image-guided surgery scenarios where surgical tools and implants frequently occur in the field-of-view. Traditional metal artifact correction methods typically use algorithmic solutions to interpolate over the highly attenuated projection measurements where metal is present but cannot recover the missing information obstructed by the metal. In this work, we treat metal artifacts as a missing data problem and employ noncircular orbits to maximize data completeness in the presence of metal. We first implement a local data completeness metric based on Tuy's condition as the percentage of great circles sampled by a particular orbit and accounted for the presence of metal by discounting any rays that pass through metal. We then compute the metric over many locations and many possible metal locations to reflect data completeness for arbitrary metal placements within a volume of interest. We used this metric to evaluate the effectiveness of sinusoidal orbits of different magnitudes and frequencies in metal artifact reduction. We also evaluated noncircular orbits in two imaging systems for phantoms with different metal objects and metal arrangements. Among a circular, tilted circular, and a sinusoidal orbit of two cycles per rotation, the latter is shown to most effectively remove metal artifacts. The noncircular orbit not only reduce the extent of streaks, but allows better visualization of spatial frequencies that cannot be recovered by metal artifact correction algorithms. These results illustrate the potential of relatively simple noncircular orbits to be robust against metal implants which ordinarily present significant challenges in interventional imaging.

Perturbation Response of Model-based Material Decomposition with Edge-Preserving Penalties

Spectral CT permits material discrimination beyond the structural information in conventional single-energy CT. Model-based material decomposition facilitates direct estimation of material density from spectral measurements, incorporating a general forward model for arbitrary spectral CT system, a statistical model of spectral CT measurements, and flexible regularization schemes. Such one-step approaches are promising for superior image quality, but the relationship between regularization parameters, imaging conditions, and reconstructed image properties is complicated. More specifically, the estimator is inherently nonlinear and may include additional nonlinearities like edge-preserving regularization, making image quality metrics intended for linear system evaluation difficult to apply. In this work, we seek approaches to quantify the image properties of this inherently nonlinear process through an investigation of perturbation response - the generalized system response to a local perturbation of arbitrary shape, location, and contrast. Such responses include cross-talk between material density channels, and we investigate the application of this metric in a sample spectral CT system. Inspired by the prior work under assumptions of local linearity and shift-invariant we also propose a prediction framework for perturbation response using a perceptron neural network. The proposed prediction framework offers an alternative to exhaustive evaluation and is a potential tool that can be used to prospectively choose optimal regularization parameters based on imaging conditions and diagnostic task.

Combining Spectral CT Acquisition Methods for High-Sensitivity Material Decomposition

Quantitative estimation of contrast agent concentration is made possible by spectral CT and material decomposition. There are several approaches to modulate the sensitivity of the imaging system to obtain the different spectral channels required for decomposition. Spectral CT technologies that enable this varied sensitivity include source kV-switching, dual-layer detectors, and source-side filtering (e.g., tiled spatial-spectral filters). In this work, we use an advanced physical model to simulate these three spectral CT strategies as well as hybrid acquisitions using all combinations of two or three strategies. We apply model-based material decomposition to a water-iodine phantom with iodine concentrations from 0.1 to 5.0 mg/mL. We present bias-noise plots for the different combinations of spectral techniques and show that combined approaches permit diversity in spectral sensitivity and improve low concentration imaging performance relative to the those strategies applied individually. Better ability to estimate low concentrations of contrast agent has the potential to reduce risks associated with contrast administration (by lowering dosage) or to extend imaging applications into targets with much lower uptake.

Performance Assessment of Texture Reproduction in High-Resolution CT

Assessment of computed tomography (CT) images can be complex due to a number of dependencies that affect system performance. In particular, it is well-known that noise in CT is object-dependent. Such object-dependence can be more pronounced and extend to resolution and image textures with the increasing adoption of model-based reconstruction and processing with machine learning methods. Moreover, such processing is often inherently nonlinear complicating assessments with simple measures of spatial resolution, etc. Similarly, recent advances in CT system design have attempted to improve fine resolution details - e.g., with newer detectors, smaller focal spots, etc. Recognizing these trends, there is a greater need for imaging assessment that are considering specific features of interest that can be placed within an anthropomorphic phantom for realistic emulation and evaluation. In this work, we devise a methodology for 3D-printing phantom inserts using procedural texture generation for evaluation of performance of high-resolution CT systems. Accurate representations of texture have previously been a hindrance to adoption of processing methods like model-based reconstruction, and texture serves as an important diagnostic feature (e.g. heterogeneity of lesions is a marker for malignancy). We consider the ability of different systems to reproduce various textures (as a function of the intrinsic feature sizes of the texture), comparing microCT, cone-beam CT, and diagnostic CT using normal- and high-resolution modes. We expect that this general methodology will provide a pathway for repeatable and robust assessments of different imaging systems and processing methods.

Non-circular CT orbit design for elimination of metal artifacts

Metal artifacts are a well-known problem in computed tomography - particularly in interventional imaging where surgical tools and hardware are often found in the field-of-view. An increasing number of interventional imaging systems are capable of non-circular orbits providing one potential avenue to avoid metal artifacts entirely by careful design of the orbital trajectory. In this work, we propose a general design methodology to find complete data solution by applying Tuy's condition for data completeness. That is, because metal implants effectively cause missing data in projections, we propose to find orbital designs that will not have missing data based on arbitrary placement of metal within the imaging field-of-view. We present the design process for these missing-data-free orbits and evaluate the orbital designs in simulation experiments. The resulting orbits are highly robust to metal objects and show greatly improved visualization of features that are ordinarily obscured.

Model-based Material Decomposition with System Blur Modeling

In this work, we present a novel model-based material decomposition (MBMD) approach for x-ray CT that includes system blur in the measurement model. Such processing has the potential to extend spatial resolution in material density estimates - particularly in systems where different spectral channels exhibit different spatial resolutions. We illustrate this new approach for a dual-layer detector x-ray CT and compare MBMD algorithms with and without blur in the reconstruction forward model. Both qualitative and quantitative comparisons of performance with and without blur modeling are reported. We find that blur modeling yields images with better recovery of high-resolution structures in an investigation of reconstructed line pairs as well as lower cross-talk bias between material bases that is ordinarily found due to mismatches in spatial resolution between spectral channels. The extended spatial resolution of the material decompositions has potential application in a range of high-resolution clinical tasks and spectral CT systems where spectral channels exhibit different spatial resolutions.

Prospective Prediction and Control of Image Properties in Model-based Material Decomposition for Spectral CT

Model-based material decomposition (MBMD) directly estimates the material densities from the spectral CT data and has found opportunities for dose reduction via physical and statistical modeling and advanced regularization. However, image properties such as spatial resolution, noise, and cross-basis response in the context of material decomposition are dependent on regularization, and high-dimensional exhaustive sweeping of regularization parameters is suboptimal. In this work, we proposed a set of prediction tools for generalized local impulse response (LIR) that characterizes both in-basis spatial resolution and cross-basis response, and noise correlation prospectively. The accuracy of noise predictor was validated in a simulation study, comparing predicted and measured in- and cross-basis noise correlations. Employing these predictors, we composed a specialized regularization for cross-talk reduction and showed that such prediction tools are promising for task-based optimization in spectral CT applications.

An Investigation of Slot-scanning for Mammography and Breast CT

Mammography and breast CT are important tools for breast cancer screening and diagnosis. Current implementations are limited by scattered radiation and/or spatial resolution. In this work, we propose and develop a slot scan-based system to be used in both mammography and CT mode that can limit scatter and collect sparse CT data for improved image quality at low radiation exposures. Monte Carlo simulations of an anthropomorphic breast phantom show a factor of 10 reduction in scattering amplitude with our slot scan-based system compared to that of a full-field detector mammography system (area mode). Similarly, slot-scan improved the MTF (particularly the low-frequency response) compared to an area detector. Investigation of sparse CT sampling with doubly sparse acquisition data return better quality reconstruction, for which our slot-scanning system is capable, over angle-only projection. Thus, a system with the combined ability for slot-scanning mammography and slot-scanning breast CT has the potential to deliver improved dose-efficient imaging performance and become viable breast cancer screening and diagnostic tools.

Volume-of-interest imaging with dynamic fluence modulation using multiple aperture devices

Volume-of-interest (VOI) imaging is a strategy in computed tomography (CT) that restricts x-ray fluence to particular anatomical targets via dynamic beam modulation. This permits dose reduction while retaining image quality within the VOI. VOI-CT implementation has been challenged, in part, by a lack of hardware solutions for tailoring the incident fluence to the patient and anatomical site, as well as difficulties involving interior tomography reconstruction of truncated projection data. We propose a general VOI-CT imaging framework using multiple aperture devices (MADs), an emerging beam filtration scheme based on two binary x-ray filters. Location of the VOI is prescribed using two scout views at anterior-posterior (AP) and lateral perspectives. Based on a calibration of achievable fluence field patterns, MAD motion trajectories were designed using an optimization objective that seeks to maximize the relative fluence in the VOI subject to minimum fluence constraints. A modified penalized-likelihood method is developed for reconstruction of heavily truncated data using the full-field scout views to help solve the interior tomography problem. Physical experiments were conducted to show the feasibility of noncentered and elliptical VOI in two applications-spine and lung imaging. Improved dose utilization and retained image quality are validated with respect to standard full-field protocols. We observe that the contrast-to-noise ratio (CNR) is 40% higher compared with low-dose full-field scans at the same dose. The total dose reduction is 50% for equivalent image quality (CNR) within the VOI.

Comparison of deep learning and human observer performance for detection and characterization of simulated lesions

Detection and characterization of abnormalities in clinical imaging are of utmost importance for patient diagnosis and treatment. We present a comparison of convolutional neural network (CNN) and human observer performance on a simulated lesion detection and characterization task. We apply both conventional performance metrics, including accuracy and nonconventional metrics such as lift charts to perform qualitative and quantitative comparisons of each type of observer. It is determined that the CNN generally outperforms the human observers, particularly at high noise levels. However, high noise correlation reduces the relative performance of the CNN, and human observer performance is comparable to CNN under these conditions. These findings extend into the field of diagnostic radiology, where the adoption of deep learning is starting to become widespread. Consideration of the applications for which deep learning is most effective is of critical importance to this development.

Performance Analysis for Nonlinear Tomographic Data Processing

Image quality analysis of nonlinear algorithms is challenging due to numerous dependencies on the imaging system, algorithmic parameters, object, and stimulus. In particular, traditional notions of linearity and local linearity are of limited utility when the system response is dependent on the stimulus itself. In this work, we analyze the performance of nonlinear systems using perturbation response - the difference between the mean output with and without a stimulus, and introduce a new metric to examine variation of the responses in individual images. We applied the analysis to four algorithms with different degrees of nonlinearity for a spherical stimulus of varying contrast. For model-based reconstruction methods [penalized-likelihood (PL) reconstruction with a quadratic penalty and a Huber penalty], perturbation response analysis reaffirmed known trends in terms of object- and location-dependence. For a CNN denoising network, the response exhibits highly nonlinear behavior as the contrast increases - from the stimulus completely disappearing, to appearing at the right contrast but smaller in size, to being fully admitted by the algorithm. Furthermore, the variation metric for PL reconstruction with a Huber penalty and the CNN network reveals high variation at the edge of the stimulus, i.e., perturbation response computed from the mean images is a smoothed version of individual responses due to "jitter" in edges. This behavior suggests that the mean response alone may not be representative of performance in individual images and image quality metrics traditionally defined based on the mean response may be inappropriate for certain nonlinear algorithms. This work demonstrates the potential utility of perturbation response and response variation in the analysis and optimization of nonlinear imaging algorithms.

Dynamic fluence field modulation in computed tomography using multiple aperture devices

A novel beam filter consisting of multiple aperture devices (MADs) has been developed for dynamic fluence field modulation (FFM) in CT. Each MAD achieves spatial modulation of x-ray through fine-scale, highly attenuating tungsten bars of varying widths and spacings. Moiré patterns produced by relative motions between two MADs provide versatile classes of modulation profiles. The dual-MAD filter can be designed to achieve specific classes of target profiles. The designed filter was manufactured through a laser-sintering process and integrated to an experimental imaging system that enables linear actuation of the MADs. Dynamic FFM was achieved through a combination of beam shape modulation (by relative MAD motion) and amplitude modulation (by view-dependent mAs). To correct for gains associated with the MADs, we developed an algorithm to account for possible focal spot changes during/between scans and spectral effects introduced by the MADs. We performed FFM designs for phantoms following two imaging objectives: (1) to achieve minimum mean variance in filtered backprojection (FBP) reconstruction, and (2) to flatten the fluence behind the phantom. Comparisons with conventional FFM strategies involving a static bowtie and pulse width modulation were performed. The dual-MAD filter produced modulation profiles closely matched with the design target, providing varying beam widths not achievable by the static bowtie. The entire range of modulation profiles was achieved by 0.373 mm of MAD displacement. The correction algorithm effectively alleviated ring artifacts as a result of MADs while preserving phantom details such as wires and tissue boundaries. Dynamic FFM enabled by the MADs were effective in achieving the imaging objectives and demonstrated superior FFM capabilities compared to the static bowtie. In an ellipse phantom, the FFM of objective 1 achieved the lowest mean variance in all cases investigated. The FFM of objective 2 produce nearly isotropic local noise power spectrum and homogeneous noise magnitude. The dual-MAD filter provides an effective tool for fluence control in CT to overcome limitations of conventional static bowties and to further enable patient-specific FFM studies for a wide range of dose and image quality objectives.

Generalized Prediction Framework for Reconstructed Image Properties using Neural Networks

Model-based reconstruction (MBR) algorithms in CT have demonstrated superior dose-image quality tradeoffs compared to traditional analytical methods. However, the nonlinear and data-dependent nature of these algorithms pose significant challenges for performance evaluation and parameter optimization. To address these challenges, this work presents an analysis framework for quantitative and predictive modeling of image properties in general nonlinear MBR algorithms. We propose to characterize the reconstructed appearance of arbitrary stimuli by the generalized system response function that accounts for dependence on the imaging conditions, reconstruction parameters, object, and the stimulus itself (size, contrast, location). We estimate this nonlinear function using a multilayer perceptron neural network by providing input and output pairs that samples the range of imaging parameters of interest. The feasibility of this approach was demonstrated for predicting the appearance of a spiculated lesion reconstructed by a penalized-likelihood objective with a Huber penalty in a physical phantom as a function of its location and reconstruction parameters β and δ. The generalized system response functions predicted from the trained neural network show good agreement with those computed from mean reconstructions, proving the ability of the framework in mapping out the nonlinear function for combinations of imaging parameters not present in the training data. We demonstrated utility of the framework to achieve desirable (e.g., non-blocky) lesion appearance in arbitrary locations in the phantom without the need for performing actual reconstructions. The proposed prediction framework permits efficient and quantifiable performance evaluations to provide robust control and understanding of image properties for general classes of nonlinear MBR algorithms.

Task-driven source-detector trajectories in cone-beam computed tomography: I. Theory and methods

We develop a mathematical framework for the design of orbital trajectories that are optimal to a particular imaging task (or tasks) in advanced cone-beam computed tomography systems that have the capability of general source-detector positioning. The framework allows various parameterizations of the orbit as well as constraints based on imaging system capabilities. To accommodate nonstandard system geometries, a model-based iterative reconstruction method is applied. Such algorithms generally complicate the assessment and prediction of reconstructed image properties; however, we leverage efficient implementations of analytical predictors of local noise and spatial resolution that incorporate dependencies of the reconstruction algorithm on patient anatomy, x-ray technique, and geometry. These image property predictors serve as inputs to a task-based performance metric defined by detectability index, which is optimized with respect to the orbital parameters of data acquisition. We investigate the framework of the task-driven trajectory design in several examples to examine the dependence of optimal source-detector trajectories on the imaging task (or tasks), including location and spatial-frequency dependence. A variety of multitask objectives are also investigated, and the advantages to imaging performance are quantified in simulation studies.

Volume-of-interest Imaging Using Multiple Aperture Devices

Volume-of-interest (VOI) imaging is a promising strategy for dose reduction in computed tomography (CT) while retaining image quality. However, implementation of VOI-CT has been challenged by the lack of adequate hardware and the interior tomography reconstruction problem. Multiple aperture devices (MAD) are a novel filtration scheme that can achieve x-ray fluence field modulation in a compact design with small translations. In this work, we propose a general approach for VOI imaging using MADs. MAD trajectories are designed to dynamically tailor the fluence for prescribed VOI. A penalized-likelihood reconstruction algorithm is proposed for fully truncated projections extended with scout views. Physical experiments were conducted to verify the feasibility for non-centered elliptic VOIs. Image quality and dose were estimated and compared with standard fullfield protocols. The ability of MAD-based VOI imaging to retain high image quality while significantly decreasing the total dose is demonstrated, suggesting the potential for dose reduction in clinical CT applications.

Regularization Analysis and Design for Prior-Image-Based X-Ray CT Reconstruction

Prior-image-based reconstruction (PIBR) methods have demonstrated great potential for radiation dose reduction in computed tomography applications. PIBR methods take advantage of shared anatomical information between sequential scans by incorporating a patient-specific prior image into the reconstruction objective function, often as a form of regularization. However, one major challenge with PIBR methods is how to optimally determine the prior image regularization strength which balances anatomical information from the prior image with data fitting to the current measurements. Too little prior information yields limited improvements over traditional model-based iterative reconstruction, while too much prior information can force anatomical features from the prior image not supported by the measurement data, concealing true anatomical changes. In this paper, we develop quantitative measures of the bias associated with PIBR. This bias exhibits as a fractional reconstructed contrast of the difference between the prior image and current anatomy, which is quite different from traditional reconstruction biases that are typically quantified in terms of spatial resolution or artifacts. We have derived an analytical relationship between the PIBR bias and prior image regularization strength and illustrated how this relationship can be used as a predictive tool to prospectively determine prior image regularization strength to admit specific kinds of anatomical change in the reconstruction. Because bias is dependent on local statistics, we further generalized shift-variant prior image penalties that permit uniform (shift invariant) admission of anatomical changes across the imaging field of view. We validated the mathematical framework in phantom studies and compared bias predictions with estimates based on brute force exhaustive evaluation using numerous iterative reconstructions across regularization values. The experimental results demonstrate that the proposed analytical approach can predict the bias-regularization relationship accurately, allowing for prospective determination of the prior image regularization strength in PIBR. Thus, the proposed approach provides an important tool for controlling image quality of PIBR methods in a reliable, robust, and efficient fashion.

Predicting image properties in penalized-likelihood reconstructions of flat-panel CBCT

PURPOSE

Model-based iterative reconstruction (MBIR) algorithms such as penalized-likelihood (PL) methods exhibit data-dependent and shift-variant properties. Image quality predictors have been derived to prospectively estimate local noise and spatial resolution, facilitating both system hardware design and tuning of reconstruction methods. However, current MBIR image quality predictors rely on idealized system models, ignoring physical blurring effects and noise correlations found in real systems. In this work, we develop and validate a new set of predictors using a physical system model specific to flat-panel cone-beam CT (FP-CBCT).

METHODS

Physical models appropriate for integration with MBIR analysis are developed and parameterized to represent nonidealities in FP projection data including focal spot blur, scintillator blur, detector aperture effect, and noise correlations. Flat-panel-specific predictors for local spatial resolution and local noise properties in PL reconstructions are developed based on these realistic physical models. Estimation accuracy of conventional (idealized) and FP-specific predictors is investigated and validated against experimental CBCT measurements using specialized phantoms.

RESULTS

Validation studies show that flat-panel-specific predictors can accurately estimate the local spatial resolution and noise properties, while conventional predictors show significant deviations in the magnitude and scale of the spatial resolution and local noise. The proposed predictors show accurate estimations over a range of imaging conditions including varying x-ray technique and regularization strength. The conventional spatial resolution prediction is sharper than ground truth. Using conventional spatial resolution predictor, the full width at half maximum (FWHM) of local point spread function (PSF) is underestimated by 0.2 mm. This mismatch is mostly eliminated in FP-specific prediction. The general shape and amplitude of local noise power spectrum (NPS) FP-specific predictions are consistent with measurement, while the conventional predictions underestimated the noise level by 70%.

CONCLUSION

The proposed image quality predictors permit accurate estimation of local spatial resolution and noise properties for PL reconstruction, accounting for dependencies on the system geometry, x-ray technique, and patient-specific anatomy in real FP-CBCT. Such tools enable prospective analysis of image quality for a range of goals including novel system and acquisition design, adaptive and task-driven imaging, and tuning of MBIR for robust and reliable behavior.

Dynamic fluence field modulation for miscentered patients in computed tomography

Traditional CT image acquisition uses bowtie filters to reduce dose, x-ray scatter, and detector dynamic range requirements. However, accurate patient centering within the bore of the CT scanner takes time and is often difficult to achieve precisely. Patient miscentering combined with a static bowtie filter can result in significant increases in dose, reconstruction noise, and CT number variations, and consequently raise overall exposure requirements. Approaches to estimate the patient position from scout scans and perform dynamic spatial beam filtration during acquisition are developed and applied in physical experiments on a CT test bench using different beam filtration strategies. While various dynamic beam modulation strategies have been developed, we focus on two approaches: (1) a simple approach using attenuation-based beam modulation using a translating bowtie filter and (2) dynamic beam modulation using multiple aperture devices (MADs)-an emerging beam filtration strategy based on binary filtration of the x-ray beam using variable width slits in a high-density beam blocker. Improved dose utilization and more consistent image performance with respect to an unmodulated baseline (static filter) are demonstrated for miscentered objects and dynamic beam filtration in physical experiments. For a homogeneous object miscentered by 4 cm, the dynamic filter reduced the maximum regional noise and dose penalties (compared with a centered object) from 173% to 16% and 42% to 14%, respectively, for a traditional bowtie, 29% to 8% and 24% to 15%, respectively, for a single MAD, and 275% to 11% and 56% to 18%, respectively, for a dual-MAD filter. The proposed methodology has the potential to relax patient centering requirements within the scanner, reduce setup time, and facilitate additional CT dose reduction.